Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3676
Missing cells6705
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory632.0 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1801 (49.0%) missing valuesMissing
built_up_area has 1986 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73144638)Skewed
built_up_area is highly skewed (γ1 = 40.70657243)Skewed
carpet_area is highly skewed (γ1 = 24.32675538)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 480 (13.1%) zerosZeros

Reproduction

Analysis started2024-08-21 15:46:20.271924
Analysis finished2024-08-21 15:46:24.733566
Duration4.46 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.5 KiB
flat
2817 
house
859 

Length

Max length5
Median length4
Mean length4.2336779
Min length4

Characters and Unicode

Total characters15563
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowhouse
5th rowhouse

Common Values

ValueCountFrequency (%)
flat 2817
76.6%
house 859
 
23.4%

Length

2024-08-21T21:16:24.763741image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:24.798665image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
flat 2817
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15563
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15563
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2817
18.1%
l 2817
18.1%
a 2817
18.1%
t 2817
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct675
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.3 KiB
2024-08-21T21:16:24.875037image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length49
Median length37
Mean length16.723265
Min length1

Characters and Unicode

Total characters61458
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)8.6%

Sample

1st rowsignature global city
2nd rowbptp terra
3rd rowambience lagoon
4th rowmalibu towne
5th rowindependent
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.7%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
mm 152
 
1.6%
global 152
 
1.6%
signature 149
 
1.6%
heights 134
 
1.4%
Other values (762) 7365
77.2%
2024-08-21T21:16:25.021871image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6707
 
10.9%
5883
 
9.6%
a 5857
 
9.5%
r 4168
 
6.8%
n 4161
 
6.8%
i 3829
 
6.2%
t 3718
 
6.0%
s 3471
 
5.6%
l 2941
 
4.8%
o 2753
 
4.5%
Other values (30) 17970
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55437
90.2%
Space Separator 5883
 
9.6%
Decimal Number 120
 
0.2%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6707
12.1%
a 5857
 
10.6%
r 4168
 
7.5%
n 4161
 
7.5%
i 3829
 
6.9%
t 3718
 
6.7%
s 3471
 
6.3%
l 2941
 
5.3%
o 2753
 
5.0%
d 2487
 
4.5%
Other values (16) 15345
27.7%
Decimal Number
ValueCountFrequency (%)
1 41
34.2%
2 35
29.2%
3 12
 
10.0%
4 11
 
9.2%
6 7
 
5.8%
5 5
 
4.2%
8 4
 
3.3%
7 4
 
3.3%
9 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
5883
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55437
90.2%
Common 6021
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6707
12.1%
a 5857
 
10.6%
r 4168
 
7.5%
n 4161
 
7.5%
i 3829
 
6.9%
t 3718
 
6.7%
s 3471
 
6.3%
l 2941
 
5.3%
o 2753
 
5.0%
d 2487
 
4.5%
Other values (16) 15345
27.7%
Common
ValueCountFrequency (%)
5883
97.7%
1 41
 
0.7%
2 35
 
0.6%
3 12
 
0.2%
4 11
 
0.2%
- 8
 
0.1%
6 7
 
0.1%
, 7
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
Other values (4) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6707
 
10.9%
5883
 
9.6%
a 5857
 
9.5%
r 4168
 
6.8%
n 4161
 
6.8%
i 3829
 
6.2%
t 3718
 
6.0%
s 3471
 
5.6%
l 2941
 
4.8%
o 2753
 
4.5%
Other values (30) 17970
29.2%

sector
Text

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.8 KiB
2024-08-21T21:16:25.104852image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3210011
Min length7

Characters and Unicode

Total characters34264
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 92
2nd rowsector 37d
3rd rowsector 24
4th rowsector 47
5th rowsector 7
ValueCountFrequency (%)
sector 3451
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (106) 2914
39.5%
2024-08-21T21:16:25.229268image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6197
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23293
68.0%
Decimal Number 7267
 
21.2%
Space Separator 3704
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3806
16.3%
s 3696
15.9%
r 3696
15.9%
e 3541
15.2%
c 3502
15.0%
t 3462
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1076
14.8%
0 804
11.1%
8 780
10.7%
9 763
10.5%
6 742
10.2%
7 684
9.4%
2 676
9.3%
3 665
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
ValueCountFrequency (%)
3704
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23293
68.0%
Common 10971
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3806
16.3%
s 3696
15.9%
r 3696
15.9%
e 3541
15.2%
c 3502
15.0%
t 3462
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
ValueCountFrequency (%)
3704
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 763
 
7.0%
6 742
 
6.8%
7 684
 
6.2%
2 676
 
6.2%
3 665
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3806
11.1%
3704
10.8%
s 3696
10.8%
r 3696
10.8%
e 3541
10.3%
c 3502
10.2%
t 3462
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6197
18.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct548
Distinct (%)15.0%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.5336776
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:25.283948image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.980614
Coefficient of variation (CV)1.1763983
Kurtosis14.933534
Mean2.5336776
Median Absolute Deviation (MAD)0.7205
Skewness3.2791947
Sum9273.2601
Variance8.8840599
MonotonicityNot monotonic
2024-08-21T21:16:25.331096image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
1.1 62
 
1.7%
0.9 61
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
2 52
 
1.4%
0.95 50
 
1.4%
1.6 48
 
1.3%
Other values (538) 3062
83.3%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.175 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 7
0.2%
0.205 1
 
< 0.1%
0.21 6
0.2%
0.22 8
0.2%
0.235 1
 
< 0.1%
0.24 5
0.1%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2651
Distinct (%)72.4%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:25.377222image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2024-08-21T21:16:25.423832image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3509
95.5%
(Missing) 16
 
0.4%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1286
Distinct (%)35.1%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2888.391
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:25.472480image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11230
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation23167.349
Coefficient of variation (CV)8.0208494
Kurtosis942.0538
Mean2888.391
Median Absolute Deviation (MAD)533
Skewness29.731446
Sum10571511
Variance5.3672607 × 108
MonotonicityNot monotonic
2024-08-21T21:16:25.518713image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 50
 
1.4%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
900 39
 
1.1%
2700 39
 
1.1%
2000 33
 
0.9%
2250 25
 
0.7%
1300 25
 
0.7%
Other values (1276) 3262
88.7%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 1
< 0.1%
65261 1
< 0.1%
64655 1
< 0.1%
Distinct2354
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.1 KiB
2024-08-21T21:16:25.616946image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.241839
Min length12

Characters and Unicode

Total characters199393
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1848 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 1010(93.83 sq.m.)Carpet area: 700 sq.ft. (65.03 sq.m.)
2nd rowSuper Built up area 2191(203.55 sq.m.)
3rd rowSuper Built up area 3200(297.29 sq.m.)Carpet area: 3156 sq.ft. (293.2 sq.m.)
4th rowBuilt Up area: 1600 (148.64 sq.m.)
5th rowPlot area 477(44.31 sq.m.)
ValueCountFrequency (%)
area 5572
18.5%
sq.m 3654
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 682
 
2.3%
plot 681
 
2.3%
Other values (2844) 8698
28.9%
2024-08-21T21:16:25.778360image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9203
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82333
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82746
41.5%
Decimal Number 47126
23.6%
Space Separator 26460
 
13.3%
Other Punctuation 23401
 
11.7%
Uppercase Letter 8592
 
4.3%
Close Punctuation 5534
 
2.8%
Open Punctuation 5534
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13151
15.9%
r 9454
11.4%
e 9318
11.3%
s 7566
9.1%
q 7430
9.0%
t 7323
8.8%
u 6770
8.2%
p 6766
8.2%
m 5543
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9203
19.5%
0 6628
14.1%
2 5687
12.1%
5 4712
10.0%
3 3959
8.4%
4 3711
7.9%
6 3673
 
7.8%
7 3252
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1871
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20385
87.1%
: 3016
 
12.9%
Space Separator
ValueCountFrequency (%)
26460
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108055
54.2%
Latin 91338
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13151
14.4%
r 9454
10.4%
e 9318
10.2%
s 7566
8.3%
q 7430
8.1%
t 7323
8.0%
u 6770
7.4%
p 6766
7.4%
m 5543
 
6.1%
l 3701
 
4.1%
Other values (10) 14316
15.7%
Common
ValueCountFrequency (%)
26460
24.5%
. 20385
18.9%
1 9203
 
8.5%
0 6628
 
6.1%
2 5687
 
5.3%
) 5534
 
5.1%
( 5534
 
5.1%
5 4712
 
4.4%
3 3959
 
3.7%
4 3711
 
3.4%
Other values (5) 16242
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26460
 
13.3%
. 20385
 
10.2%
a 13151
 
6.6%
r 9454
 
4.7%
e 9318
 
4.7%
1 9203
 
4.6%
s 7566
 
3.8%
q 7430
 
3.7%
t 7323
 
3.7%
u 6770
 
3.4%
Other values (25) 82333
41.3%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3604461
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:25.828788image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8977544
Coefficient of variation (CV)0.56473288
Kurtosis18.210248
Mean3.3604461
Median Absolute Deviation (MAD)1
Skewness3.484914
Sum12353
Variance3.6014718
MonotonicityNot monotonic
2024-08-21T21:16:25.869786image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 941
25.6%
4 660
18.0%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 941
25.6%
3 1496
40.7%
4 660
18.0%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4249184
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:25.910683image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9481914
Coefficient of variation (CV)0.56882856
Kurtosis17.540029
Mean3.4249184
Median Absolute Deviation (MAD)1
Skewness3.2486068
Sum12590
Variance3.7954497
MonotonicityNot monotonic
2024-08-21T21:16:25.951371image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1046
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1046
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1172 
3
1073 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3188248
Min length1

Characters and Unicode

Total characters4848
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row3+
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1073
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
5.0%

Length

2024-08-21T21:16:25.995251image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.031899image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
3 2245
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 2245
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2245
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
5.0%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2245
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2245
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7971014
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:26.075197image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0128748
Coefficient of variation (CV)0.88462337
Kurtosis4.516579
Mean6.7971014
Median Absolute Deviation (MAD)3
Skewness1.6942871
Sum24857
Variance36.154663
MonotonicityNot monotonic
2024-08-21T21:16:26.121313image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 936
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size258.1 KiB
North-East
623 
East
622 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8392246
Min length4

Characters and Unicode

Total characters17994
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth-East
2nd rowNorth-East
3rd rowNorth
4th rowEast
5th rowNorth-West

Common Values

ValueCountFrequency (%)
North-East 623
16.9%
East 622
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.3%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-08-21T21:16:26.165539image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.205952image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
north-east 623
23.7%
east 622
23.6%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3773
21.0%
s 2013
11.2%
o 1760
9.8%
h 1760
9.8%
E 1418
 
7.9%
a 1418
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13079
72.7%
Uppercase Letter 3773
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3773
28.8%
s 2013
15.4%
o 1760
13.5%
h 1760
13.5%
a 1418
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1418
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16852
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3773
22.4%
s 2013
11.9%
o 1760
10.4%
h 1760
10.4%
E 1418
 
8.4%
a 1418
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3773
21.0%
s 2013
11.2%
o 1760
9.8%
h 1760
9.8%
E 1418
 
7.9%
a 1418
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.384657
Min length9

Characters and Unicode

Total characters49202
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder Construction
2nd rowRelatively New
3rd rowOld Property
4th rowNew Property
5th rowOld Property

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 265
 
7.2%

Length

2024-08-21T21:16:26.249956image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.287807image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.4%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 265
 
3.8%
construction 265
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8430
17.1%
l 4721
 
9.6%
t 3635
 
7.4%
3370
 
6.8%
y 3105
 
6.3%
r 2885
 
5.9%
d 2306
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2217
 
4.5%
Other values (15) 14055
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38786
78.8%
Uppercase Letter 7046
 
14.3%
Space Separator 3370
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8430
21.7%
l 4721
12.2%
t 3635
9.4%
y 3105
 
8.0%
r 2885
 
7.4%
d 2306
 
5.9%
w 2239
 
5.8%
i 2217
 
5.7%
a 2209
 
5.7%
o 1989
 
5.1%
Other values (7) 5050
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.4%
P 896
12.7%
O 866
 
12.3%
U 571
 
8.1%
M 563
 
8.0%
C 265
 
3.8%
Space Separator
ValueCountFrequency (%)
3370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45832
93.2%
Common 3370
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8430
18.4%
l 4721
 
10.3%
t 3635
 
7.9%
y 3105
 
6.8%
r 2885
 
6.3%
d 2306
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2217
 
4.8%
a 2209
 
4.8%
Other values (14) 11846
25.8%
Common
ValueCountFrequency (%)
3370
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8430
17.1%
l 4721
 
9.6%
t 3635
 
7.4%
3370
 
6.8%
y 3105
 
6.3%
r 2885
 
5.9%
d 2306
 
4.7%
N 2239
 
4.6%
w 2239
 
4.6%
i 2217
 
4.5%
Other values (15) 14055
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1801
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:26.337108image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.6
Variance583959.12
MonotonicityNot monotonic
2024-08-21T21:16:26.385806image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.5%
(Missing) 1801
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1986
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:26.431220image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2024-08-21T21:16:26.478933image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1986
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct732
Distinct (%)39.1%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2530.2228
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:26.525659image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22805.887
Coefficient of variation (CV)9.0133909
Kurtosis604.21426
Mean2530.2228
Median Absolute Deviation (MAD)470
Skewness24.326755
Sum4734046.9
Variance5.2010849 × 108
MonotonicityNot monotonic
2024-08-21T21:16:26.572843image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (722) 1577
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
2971 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Length

2024-08-21T21:16:26.615737image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.649398image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2971
80.8%
1 705
 
19.2%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
2348 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

Length

2024-08-21T21:16:26.684737image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.717710image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2348
63.9%
1 1328
36.1%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3338 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Length

2024-08-21T21:16:26.754015image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.786955image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3338
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3020 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Length

2024-08-21T21:16:26.934197image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:26.967349image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3020
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
3271 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Length

2024-08-21T21:16:27.003452image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:27.036305image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3271
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.9 KiB
0
2415 
2
1055 
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3676
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

Length

2024-08-21T21:16:27.071479image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T21:16:27.106089image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2415
65.7%
2 1055
28.7%
1 206
 
5.6%

luxury_score
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.234494
Minimum0
Maximum174
Zeros480
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size57.4 KiB
2024-08-21T21:16:27.145671image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.278756
Coefficient of variation (CV)0.74793478
Kurtosis-0.8893174
Mean71.234494
Median Absolute Deviation (MAD)38
Skewness0.45765497
Sum261858
Variance2838.6258
MonotonicityNot monotonic
2024-08-21T21:16:27.192884image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 480
 
13.1%
49 347
 
9.4%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (150) 2295
62.4%
ValueCountFrequency (%)
0 480
13.1%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-08-21T21:16:24.061731image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:20.709077image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.188807image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.664317image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.997543image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.353297image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.707818image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.043128image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.375269image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.722388image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.096450image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:20.767441image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.223084image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.696481image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.032738image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.387986image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.740376image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.075048image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.410858image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.755421image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.131230image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:20.833016image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.258488image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.729661image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.068464image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.424536image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.774125image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.110402image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.446275image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.790444image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.163332image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:20.906648image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.290432image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.759691image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.101337image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.456989image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.805423image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.142307image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.479819image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.824187image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.200937image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:20.979945image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.328324image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.794669image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.138866image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.495334image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.840873image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.179309image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.517418image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.860124image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.237801image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.018790image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.365891image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.830563image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.175590image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.532271image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.876828image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.212586image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.556049image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.895532image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.271210image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.051435image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.399737image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.861934image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.210005image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.566436image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.907636image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.244311image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.589389image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.927171image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.304648image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.083389image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.557060image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.894563image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.244076image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.598932image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.940192image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.277019image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.618402image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.961770image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.454506image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.119879image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.593876image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.928728image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.281685image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.636384image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.975230image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.306118image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.654997image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.991837image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.489908image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.153316image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.628365image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:21.963629image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.316511image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:22.671163image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.008229image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.340687image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:23.685019image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-21T21:16:24.026218image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-21T21:16:27.232845image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2740.1110.1300.0000.0000.0910.1260.2140.2600.1080.1870.1020.0560.3790.2860.1420.1400.086
area0.0001.0000.0110.6870.6240.8350.8010.0220.1160.0430.2600.0420.0370.7440.2060.0280.0150.0390.0180.948
balcony0.2740.0111.0000.2250.1760.0000.0260.0170.0790.1780.2250.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1110.6870.2251.0000.8620.4650.5990.044-0.0040.1980.1830.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1300.6240.1760.8621.0000.3800.5680.032-0.1030.1670.0620.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4650.3801.0000.9691.0000.0910.0880.2910.0000.0000.6040.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5680.9691.0000.0000.1590.0000.2370.0160.0000.6130.1360.0000.0000.0000.0020.894
facing0.0910.0220.0170.0440.0321.0000.0001.0000.0000.0490.0650.0000.0290.0210.0000.0940.0350.0360.0000.000
floorNum0.1260.1160.079-0.004-0.1030.0910.1590.0001.0000.0160.2250.0330.1020.001-0.1260.4850.0840.1120.0780.152
furnishing_type0.2140.0430.1780.1980.1670.0880.0000.0490.0161.0000.2450.0590.2160.1750.0220.0800.2700.1560.1410.134
luxury_score0.2600.2600.2250.1830.0620.2910.2370.0650.2250.2451.0000.1770.1910.2130.0500.3200.3500.2290.1840.223
others0.1080.0420.0820.0700.0790.0000.0160.0000.0330.0590.1771.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1870.0370.1970.2860.2910.0000.0000.0290.1020.2160.1910.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1020.7440.1360.7200.6810.6040.6130.0210.0010.1750.2130.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0560.2060.0330.4110.4170.1320.1360.000-0.1260.0220.0500.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.0940.4850.0800.3200.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2860.0150.4410.5200.3170.0000.0000.0350.0840.2700.3500.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1420.0390.1460.2440.2230.0000.0000.0360.1120.1560.2290.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1400.0180.1830.1760.1540.0000.0020.0000.0780.1410.1840.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.0860.9480.3060.8190.8000.9260.8940.0000.1520.1340.2230.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2024-08-21T21:16:24.548597image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-21T21:16:24.654423image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature global citysector 920.787722.01010.0Super Built up area 1010(93.83 sq.m.)Carpet area: 700 sq.ft. (65.03 sq.m.)2232.0NaNUnder Construction1010.0NaN700.000001024
1flatbptp terrasector 37d1.727850.02191.0Super Built up area 2191(203.55 sq.m.)333+10.0South-EastRelatively New2191.0NaNNaN01000031
2flatambience lagoonsector 244.2513281.03200.0Super Built up area 3200(297.29 sq.m.)Carpet area: 3156 sq.ft. (293.2 sq.m.)343+3.0North-EastOld Property3200.0NaN3156.0010102143
3housemalibu townesector 478.5053125.01600.0Built Up area: 1600 (148.64 sq.m.)12123+4.0NorthNew PropertyNaN1600.0NaN00000299
4houseindependentsector 70.8918658.0477.0Plot area 477(44.31 sq.m.)5322.0EastOld PropertyNaN477.0NaN0000008
5flatpioneer parksector 612.0011764.01700.0Super Built up area 1700(157.94 sq.m.)33324.0North-WestModerately Old1700.0NaNNaN00000275
6flatdlf the primussector 82a1.8013846.01300.0Super Built up area 1818(168.9 sq.m.)Carpet area: 1300 sq.ft. (120.77 sq.m.)333+10.0North-EastRelatively New1818.0NaN1300.0010001174
7flatmaruti viharsector 281.009090.01100.0Super Built up area 1100(102.19 sq.m.)1111.0NorthOld Property1100.0NaNNaN10010280
8flatmm woodshiresector 1070.805856.01366.0Super Built up area 1366(126.91 sq.m.)Carpet area: 1055 sq.ft. (98.01 sq.m.)223+6.0WestRelatively New1366.0NaN1055.0000102174
9flatbestech park view residencysector 21.517869.01919.0Super Built up area 1920(178.37 sq.m.)343+7.0South-WestModerately Old1920.0NaNNaN01000284
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3791flatdlf regal gardenssector 901.2607636.01650.0Super Built up area 1744(162.02 sq.m.)Built Up area: 1650 sq.ft. (153.29 sq.m.)33212.0NorthRelatively New1744.01650.0NaN0000108
3793flatprasha apartmentsmanesar0.7503309.02267.0Super Built up area 2115(196.49 sq.m.)333+3.0WestModerately Old2115.0NaNNaN01000031
3794flatmm merlinsector 672.90014167.02047.0Super Built up area 2047(190.17 sq.m.)3330.0WestRelatively New2047.0NaNNaN00001249
3795houseinternational city by sobha phase 2sector 1097.00012963.05400.0Plot area 600(501.68 sq.m.)443+2.0WestRelatively NewNaN5400.0NaN000002102
3796flatadani mk oyster grandesector 1022.61010046.02598.0Super Built up area 2598(241.36 sq.m.)Built Up area: 2200 sq.ft. (204.39 sq.m.)Carpet area: 2000 sq.ft. (185.81 sq.m.)33314.0North-EastRelatively New2598.02200.02000.011000021
3797flathcbs sports villesohna road0.2694345.0619.0Built Up area: 619 (57.51 sq.m.)22210.0NaNRelatively NewNaN619.0NaN00000043
3798flatireo victory valleysector 673.65013528.02698.0Super Built up area 2698(250.65 sq.m.)Built Up area: 2490 sq.ft. (231.33 sq.m.)343+18.0NorthRelatively New2698.02490.0NaN01010261
3799houseindependentsector 112.10017284.01215.0Plot area 135(112.88 sq.m.)663+3.0NaNRelatively NewNaN1215.0NaN00000014
3800flatramprastha the edge towerssector 37d0.7505319.01410.0Built Up area: 1410 (130.99 sq.m.)22319.0NaNModerately OldNaN1410.0NaN00000063
3801houseindependentsector 2618.4001859.098978.0Plot area 502(419.74 sq.m.)Carpet area: 11000 sq.yards (9197.4 sq.m.)683+4.0SouthRelatively NewNaNNaN11000.001110260